AI Predictions For 2019

Kevin Poskitt

“The horse is here to stay but the automobile is only a novelty—a fad.” 

As this quote from a banker to Horace Rackman, one of the early investors in the Henry Ford Company, proves, there is an inherent risk when making predictions that you will look very silly in a few years. But it’s also fun at the beginning of a new year to think about what the future holds. So, without further ado, here are my three predictions for where artificial intelligence (AI) will go in 2019.

Is it still all about the data?

As the saying goes: garbage in, garbage out – and the same holds true with AI. In fact, without good, trustworthy, and governed data to build models with, AI is exactly that: artificial.

In 2019, we’ll continue to see an evolution of how data is stored, managed, and governed. In particular, there is a need for a content management system for your data. For example, if you use iCloud or Google Photos, you have pictures from anywhere in the world, stored on multiple devices, but easily searchable and consumable.

The same will become true for data, be it structured, unstructured, audio, video, image, or text. AI teams will need to manipulate all this data so it can be used to build AI models. However, all their hard work should be preserved so the next AI team can find and reuse their datasets with ease.

Scale – not just for dirty faucets anymore

AI systems can help fix things before they break; they can help you understand customers better than ever; they can even talk to your customers on your behalf. But when the cost to develop, deploy, and maintain these systems is too significant, their prevalence in the real world is limited.

If it costs you $5 million to build and create an effective AI model, you will only solve problems that cost you more than $5 million. There are a limited number of $5 million-plus problems in the world, and many smaller problems that can benefit from AI techniques.

The way to address this is through automation. Scale requires that you find efficiencies and remove low-value, repetitive tasks from the equation. Intelligent robotic process automation lets you do this for your knowledge workers in key applications, and intelligent interfaces and conversational AI allows you to do this for human-to-machine interactions. But we will see an increase in solutions that allow you to do this for the underlying process of designing, deploying, and maintaining AI models.

The AI assembly line

This is nothing new: the first and second industrial revolutions were driven by the assembly line. By breaking down big jobs into smaller tasks that brought repeatability, specialization, and automation, we ensured that cars became affordable, that goods and services could be within the reach of a broader class of society, and that materials could be easily transported to anywhere in the world.

The same will happen with AI when you need to connect to the data you need, learn what the signal is from that data, scale the application across your organization, and ultimately consume that knowledge through an automated business process, a next-generation user interface, data embedded in an application, or directly in a data visualization.

What are your thoughts and predictions for how AI will evolve in 2019?

Learn more

SAP worked with more than a dozen industry experts to uncover five trends that will determine the customer experience over the next decade. “The Future Customer Experience: 5 Essential Trends” report examines each of these trends and offers recommendations for how brands should respond now to prepare. Download the report.

Kevin Poskitt

About Kevin Poskitt

Kevin Poskitt is part of SAP's product management team that is focused on machine learning, data science, and artificial intelligence. He is responsible for leading SAP's next-generation projects in unified machine learning. His experience encompasses more than 10 years in various technology companies ranging from small startups to large software vendors, where he has worked in multiple departments including sales, marketing, finance, and product management. He is a graduate of the University of Toronto with a specialty in economics and finance. He holds a bachelor's of commerce and a diploma in accounts from the University of British Columbia.